International Journal of Medical Science and Public Health Research

Features Of Pulmonary Function Impairment In Children With Cystic Fibrosis According To Computerized Spirometry Data

Inomov Bakhodir Niyamatjonovich — 2026-02-10

Objective. To assess the state of pulmonary function in children with cystic fibrosis and to determine the nature and severity of ventilatory disorders based on computerized spirometry data.

Materials and Methods. Pulmonary function was assessed in 64 children with cystic fibrosis aged 5 to 14 years who were receiving inpatient treatment. The control group included apparently healthy children and children with recurrent obstructive bronchitis. Computerized spirometry was performed using a BTL-08 Spiro Pro spirometer with evaluation of the main spirometric parameters (FEV₁, vital capacity, Tiffeneau index, MEF50).

Results. All children with cystic fibrosis demonstrated a reduction in pulmonary function parameters. The combined type of ventilatory impairment was the most frequently observed (62.5%), characterized by the presence of both restrictive and bronchial obstructive components. A statistically significant decrease in FEV₁, vital capacity, Tiffeneau index, and MEF50 was noted compared with children with recurrent obstructive bronchitis and apparently healthy peers (p<0.001). In the majority of patients, marked and severe deviations of pulmonary function indices were identified, reflecting progression of chronic bronchopulmonary involvement.

Conclusion. Children with cystic fibrosis exhibit a pronounced and statistically significant deterioration of pulmonary function parameters, predominantly of a combined ventilatory impairment type. Computerized spirometry is an informative and accessible method for assessing the severity of bronchopulmonary involvement and for monitoring the effectiveness of therapy in this patient population.

Population-Level Oral Disease Surveillance Using Large Language Models on Clinical and Public Health Data

Han Thi Ngoc Phan — 2026-02-05

Population-level oral disease surveillance is critical for guiding public health interventions, yet traditional systems relying solely on structured data often fail to capture contextual, behavioral, and access-to-care determinants embedded in unstructured clinical narratives. In this study, we developed a hybrid large language model (LLM) framework that integrates structured epidemiological features with embeddings derived from examination notes and survey text to improve the detection and monitoring of dental caries, periodontal disease, and tooth loss. Using the publicly available NHANES Oral Health Dataset, we compared the performance of traditional machine learning models, text-only LLM models, and our proposed hybrid approach. The hybrid model consistently outperformed all baselines, achieving higher accuracy, precision, recall, F1-score, and calibration, while maintaining equitable performance across demographic and socioeconomic subgroups. Explainability analyses revealed that combining structured and unstructured features captured clinically meaningful patterns, including behavioral risk factors and care access barriers. Our findings suggest that hybrid LLM-based surveillance can enhance real-time population-level monitoring, identify high-risk communities, and inform preventive strategies within the U.S. public healthcare system, offering a scalable, interpretable, and equitable approach to oral health monitoring.

Epidemiology, Diagnostic Challenges, and Clinical Outcomes of Acute Febrile Illnesses in Low-Resource Settings: Integrating Bacterial, Viral, and Parasitic Perspectives

Dr. Samuel K. Mbele — 2026-02-01

Acute febrile illness (AFI) remains one of the most pervasive and diagnostically complex clinical syndromes affecting populations in low- and middle-income countries, particularly within sub-Saharan Africa and South Asia. Fever is both a nonspecific symptom and a critical clinical signal, often masking a wide array of bacterial, viral, and parasitic etiologies whose epidemiology overlaps spatially, temporally, and symptomatically. This diagnostic ambiguity has profound consequences for patient outcomes, antimicrobial stewardship, and health system sustainability. The present article develops an extensive, integrative analysis of AFI grounded strictly in the existing body of literature provided, synthesizing epidemiological evidence, diagnostic practices, laboratory limitations, and clinical outcomes associated with febrile illnesses in resource-constrained settings. Drawing heavily from studies conducted in Tanzania, Nepal, and comparable endemic regions, this work examines the shifting etiological landscape of AFI in the post-malaria control era, where non-malarial febrile illnesses are increasingly recognized as dominant contributors to morbidity and mortality (Chipwaza et al., 2015; Crump et al., 2013; Hercik et al., 2017).

The article critically explores bacterial causes such as brucellosis, leptospirosis, enteric fever, and rickettsial infections, alongside viral pathogens including dengue and chikungunya, emphasizing their clinical overlap and diagnostic indistinguishability in early disease stages (Corbel, 2006; Debora et al., 2016; Karnik and Patankar, 2021). Particular attention is given to laboratory diagnostics, where reliance on serological assays such as Widal, Weil-Felix, and rapid diagnostic tests often introduces interpretive uncertainty and misclassification, especially in endemic settings with high background antibody prevalence (Mariraj et al., 2020; Udayan et al., 2014). The challenges of malaria diagnosis, including residual antimalarial drug detection and discrepancies between conventional microscopy and molecular techniques, are discussed as a paradigm of broader diagnostic limitations (Dahal et al., 2021; Gallay et al., 2018).

Beyond etiology and diagnosis, the article examines the clinical trajectory of severe AFI, focusing on complications such as acute respiratory distress syndrome, multi-organ dysfunction syndrome (MODS), and neurological impairment, with prognostic insights drawn from Glasgow Coma Scale-based mortality prediction studies (Li et al., 2007; Knox et al., 2014; Bastos et al., 1993). The role of inappropriate empiric therapy, antimicrobial resistance, and delayed diagnosis in exacerbating disease severity is analyzed through a mechanistic and evolutionary lens (Hasan et al., 2021). By synthesizing these diverse strands of evidence, this article argues for a reconceptualization of AFI as a syndromic entity requiring integrated diagnostic algorithms, strengthened laboratory capacity, and context-sensitive clinical decision-making frameworks. The findings underscore the urgent need for health system investments that align epidemiological realities with diagnostic and therapeutic practices, ultimately improving patient outcomes in regions where fever remains a leading cause of healthcare utilization and mortality.

The Role Of Oxidative Stress In The Development And Maintenance Of Pulmonary Hypertension In Children With Congenital Heart Disease

Ashurova Dilfuza Tashpulatovna — 2026-02-10

Background: Pulmonary hypertension is a severe and prognostically unfavorable complication of congenital heart disease in children, characterized by progressive pulmonary vascular remodeling and increased perioperative risk. Increasing evidence suggests that oxidative stress plays a significant role in pulmonary vascular dysfunction; however, its contribution to the development and persistence of pulmonary hypertension in pediatric congenital heart disease remains insufficiently defined.

Objective: To investigate the role of oxidative stress in the development and maintenance of pulmonary hypertension in children with congenital heart disease by evaluating key antioxidant defense markers and their association with pulmonary hemodynamic parameters.

Methods: This observational study included children with congenital heart disease stratified according to the presence or absence of pulmonary hypertension based on echocardiographic assessment. Superoxide dismutase activity and total antioxidant status were measured in peripheral blood serum using spectrophotometric methods. Echocardiographic parameters reflecting pulmonary vascular involvement were analyzed in parallel. Intergroup comparisons and correlation analyses were performed using appropriate statistical methods.

Results: Children with pulmonary hypertension demonstrated significantly reduced superoxide dismutase activity compared with patients without pulmonary hypertension. Total antioxidant status was also lower in the pulmonary hypertension group, indicating depletion of overall antioxidant capacity. Oxidative stress markers were inversely associated with pulmonary artery pressure, suggesting a relationship between impaired antioxidant defense and pulmonary hemodynamic severity. Partial postoperative recovery of antioxidant parameters did not result in complete normalization, indicating persistent redox imbalance.

Conclusion: Oxidative stress, manifested by reduced enzymatic and total antioxidant defense, represents a stable pathogenic component of pulmonary hypertension in children with congenital heart disease. Assessment of antioxidant markers may provide additional insight into pulmonary vascular vulnerability and contribute to improved risk stratification and targeted management strategies in this patient population.

Predicting Infectious Disease Outbreaks Using Machine Learning and Real-Time Epidemiological Data: Leverage Social Media, Environmental, And Public Health Data to Forecast Outbreaks Like Influenza, COVID-19, Or RSV

Md. Emran Hossen — 2026-02-05

Accurate and timely prediction of infectious disease outbreaks is critical for effective public health response. In this study, we developed a machine learning framework that integrates real-time epidemiological data, social media signals, environmental variables, and policy interventions to forecast influenza and COVID‑19 outbreaks. We evaluated multiple models, including logistic regression, random forest, XGBoost, and LSTM neural networks, across classification and regression tasks. XGBoost achieved the highest accuracy for influenza outbreak detection, while LSTM networks outperformed other models in forecasting COVID‑19 case counts, particularly for longer-term predictions. Feature analysis revealed that social media indicators, environmental conditions, and policy measures significantly enhanced predictive performance. The results demonstrate that multimodal machine learning models can provide early warnings, inform resource allocation, and support data-driven decision-making in the US public healthcare system. Our findings highlight the potential of integrating diverse real-time data streams with advanced machine learning techniques to strengthen epidemic preparedness and response.